Methods and systems for providing alimentary elements

ABSTRACT

A system for providing alimentary elements includes a computing device configured to receive a primary input relating to a first user for a compatible alimentary element based on biological extraction, receive secondary input from the second user for an alimentary element not associated with biological extraction, generate an extensible alimentary element display for the second user, wherein generating the extensible alimentary element display includes locating at least an alimentary element originator as a function of the secondary input and the compatible alimentary elements, generating a queue of alimentary elements from the located alimentary element originator, wherein the queue includes alimentary elements for the second user as a function of the secondary input and compatible alimentary elements for the first user, and provide a representation, via a graphical user interface, of a compatible alimentary element for the first user and an alimentary element for the second user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Non-provisional application Ser.No. 17/106,487 filed on Nov. 30, 2020 and entitled “METHODS AND SYSTEMSFOR PROVIDING ALIMENTARY ELEMENTS,” the entirety of which isincorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of graphical userinterface and document processing. In particular, the present inventionis directed to methods and systems for providing alimentary elements.

BACKGROUND

Updating graphical user interface presentation of elements aimed atimproving physiology is typically based on returning solutionsdetermined through computer software. Generating newer options as afunction of the multiplicity of feedback, such as taste, preference, andnutrition are difficult to achieve while being mindful of individualphysiology without designated inputs.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for providing alimentary elements, the systemcomprising a computing device configured to receive primary inputrelating to a first user for a compatible alimentary element, whereincompatible alimentary elements are based on biological extractionreceived from the first user, receive secondary input relating to asecond user for an alimentary element, wherein the second user is notassociated with an alimentary element program, generate an extensiblealimentary element display for the second user, wherein generating theextensible alimentary element display includes locating alimentaryelement originators as a function of the secondary input and thecompatible alimentary elements for the first user as a function of afirst position associated with the first user, and generating a queue ofalimentary elements retrieved from the located alimentary elementoriginators, wherein the queue includes alimentary elements for thesecond user as a function of compatible alimentary elements provided tothe first user, and provide a representation, via a graphical userinterface, of at least a compatible alimentary element for the firstuser and at least an alimentary element for the second user.

In another aspect, a method for providing alimentary elements, themethod comprising receiving, by a computing device, primary inputrelating to a first user for a compatible alimentary element, whereincompatible alimentary elements are based on biological extractionreceived from the first user, receiving, by the computing device,secondary input relating to a second user for an alimentary element,wherein the second user is not associated with an alimentary elementprogram, generating, by the computing device, an extensible alimentaryelement display for the second user, wherein generating the extensiblealimentary element display includes locating alimentary elementoriginators as a function of the secondary input and the compatiblealimentary elements for the first user as a function of a first positionassociated with the first user, and generating a queue of alimentaryelements retrieved from the located alimentary element originators,wherein the queue includes alimentary elements for the second user as afunction of compatible alimentary elements provided to the first user,and providing, by the computing device, a representation, via agraphical user interface, of at least a compatible alimentary elementfor the first user and at least an alimentary element for the seconduser.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for providing alimentary elements;

FIG. 2 is a block diagram illustrating an exemplary embodiment of analimentary database;

FIG. 3 is a diagrammatic representation of an exemplary embodiment of afile share label;

FIG. 4 is a diagrammatic representation of an exemplary embodiment of afirst device signaling to a second device via an audiovisualnotification;

FIGS. 5A and 5B are a diagrammatic representation of an exemplaryembodiment of a locating an alimentary element originator using radialsearch;

FIG. 6 is a block diagram illustrating an exemplary embodiment of amachine-learning module;

FIG. 7 is a flow diagram illustrating an exemplary method for providingalimentary elements; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for providing alimentary elements. In an embodiment,the system includes a computing device configured to receive input froma first user corresponding to an alimentary element that is generated asa function of the first user's biological extraction data. Computingdevice is further configured to receive input from a second user for analimentary element that does not contain the same corresponding data.The computing device is configured to generate an extensible alimentaryelement display that may generate alimentary elements for the seconduser as a function of the alimentary element originators in proximity tothe location of the two users. In an embodiment, system may generatemetrics so that second user may make an alimentary element selectionbased on nutrition, like how the first user is provided alimentaryelements.

Referring now to FIG. 1, an exemplary embodiment of a system 100 forproviding alimentary elements is illustrated. System includes acomputing device 104. Computing device 104 may include any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor and/or systemon a chip (SoC) as described in this disclosure. Computing device mayinclude, be included in, and/or communicate with a mobile device such asa mobile telephone or smartphone. Computing device 104 may include asingle computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. Computingdevice 104 may interface or communicate with one or more additionaldevices as described below in further detail via a network interfacedevice. Network interface device may be utilized for connectingcomputing device 104 to one or more of a variety of networks, and one ormore devices. Examples of a network interface device include, but arenot limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Computing device 104 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. Computing device 104 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. Computing device 104 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. Computingdevice 104 may be implemented using a “shared nothing” architecture inwhich data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

With continued reference to FIG. 1, computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, inputs and/or outputs of repetitions to produce anaggregate result, reduction or decrement of one or more variables suchas global variables, and/or division of a larger processing task into aset of iteratively addressed smaller processing tasks. Computing device104 may perform any step or sequence of steps as described in thisdisclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

Continuing in reference to FIG. 1, computing device 104 receive primaryinput relating to a first user for a compatible alimentary element,wherein compatible alimentary elements are based on biologicalextraction received from the first user. As used in this disclosure, an“alimentary element,” is a meal, grocery item, food element, beverage,nutrition supplement, edible arrangement, or the like, that may begenerated by a restaurant, cafeteria, fast food chain, grocery store,food truck, farmer's market, proprietor, convenience store, deli, or anyplace that provides the above to an individual. A “compatible alimentaryelement,” as used in this disclosure, is an alimentary element providedto an individual according to the individual's biological extractiondata, wherein alimentary elements are “compatible” based on adetermination regarding the compatibility of the alimentary elementsaccording to the biological extraction data. “Biological extraction”data, as used in this disclosure, is chemical data, physiological data,medical data, and the like. A compatible alimentary element may includealimentary elements intended to address a nutrition deficiency, reduceinflammation, improve recovery from exercise, improve overall health,among other targeted effects. A compatible alimentary element mayinclude alimentary elements provided as a function of an individual'sallergies, food intolerances, philosophical, religious, and lifestyleconsiderations, among other factors involved in selecting alimentaryelements, for instance plant-based, vegan, Kosher, and the like. Acompatible alimentary element may be generated and provided to a user asa function of a user's biological data, such as blood chemistry, forinstance blood protein and enzyme concentrations and specific activitiesfor instance of fibrinogen, ferritin, serum amyloid A, α-1-acidglycoprotein, ceruloplasmin, hepcidin, haptoglobin, tumor necrosisfactor-α (TNF-α), among other acute phase proteins; for instancecytokine identities and concentrations for instance interleukin-6(IL-6); metabolites identities and concentrations such as blood sugar,LDL and HDL cholesterol content; hormone identities and concentrationssuch as insulin, androgens, cortisol, thyroid hormones, and the like;erythrocyte sedimentation rate, blood cell counts, plasma viscosity, andother biochemical, biophysical, and physiological properties regardingblood panels, blood tests, and the like, for instance and withoutlimitation as it relates to biomarkers of inflammation. Alimentaryelements may be recommended to a user as a function of these biologicalextraction data with the intention of modifying the biologicalextraction data, for instance by lowering blood sugar, decreasing LDLcholesterol levels, reducing pro-inflammatory biomarkers, reducing freeradicals and oxidative damage, among other targeted effects ofalimentary elements on user biological extraction.

Continuing in reference to FIG. 1, in non-limiting illustrativeexamples, biomarkers of inflammation may include biochemical propertiesspecific to a user such as the level of inflammation as evidence by thepresence and concentration of inflammatory biomarkers,post-translational modification of proteins, epigenetic markers, etc.,and alimentary elements may be identified and provided to a user tofocus on reducing inflammation for instance and without limitation, asdescribed in U.S. Nonprovisional application Ser. No. 17/007,251 filedAug. 31, 2020 titled “METHOD OF SYSTEM FOR REVERSING INFLAMMATION IN AUSER,” the entirety of which is incorporated herein by reference. Thelevel of inflammation, or any biochemical ailment and/or property of auser may be enumerated, and based on the numerical value, an alimentaryelement may be recommended to the user. Alternatively or additionally,user biological extraction may be used as an input for determiningalimentary element recommendations that improve the user's health statebased on the user's biological extraction, for instance and withoutlimitation, as described in U.S. Nonprovisional application Ser. No.16/375,303 filed Apr. 4, 2020 titled “SYSTEMS AND METHODS FOR GENERATINGALIMENTARY INSTRUCTION SETS BASED ON VIBRANT CONSTITUTION GUIDANCE,” theentirety of which is incorporated herein by reference.

Continuing in reference to FIG. 1, computing device 104 is configured toreceive primary input relating to a first user for a compatiblealimentary element. As described above, compatible alimentary elementsmay be provided to a user as a function of a user's individualalimentary element program, which may be informed by a user's biologicalextraction data. An “alimentary element program,” as used in thisdisclosure, is a plurality of compatible alimentary elements as definedabove, which a user may be informed to select based on a user'sbiological extraction, including medical data, physiology, demographics,lifestyle, and the like. An alimentary element program 108 may include,for instance and without limitation, an instruction set that a computingdevice 104 may provide to a user concerning alimentary elements that mayimprove the user's health state, including meal-types, macronutrientamounts, nutrient quantities, appropriate times to eat, among otherdata. An alimentary element program 108 may include alimentary elementsa user is expected to substitute to avoid ailments such as allergies,food intolerances, inflammation, and the like. An alimentary elementprogram 108 may include alimentary elements a user is expected toinclude in their diet to address nutrition deficiencies, symptoms,diseases, and the like. In non-limiting illustrative examples, analimentary element program 108 may be associated with an audiovisualnotification, wherein the notification is used by computing device 104to provide a compatible alimentary element obtained from the alimentaryelement program 108 directed to be displayed to the user via a userdevice, such as a “smartphone”, laptop, tablet computer,internet-of-things (IOT) device, and the like.

Continuing in reference to FIG. 1, a “primary input,” as used in thisdisclosure, is an input for receiving an alimentary element, forinstance from an alimentary element program 108, by a user that hascompatible alimentary elements. Primary input 112 may include user inputfrom a graphical user interface. A “graphical user interface,” as usedin this disclosure, is any form of a user interface that allows asubject to interface with an electronic device through graphical icons,audio indicators, text-based interface, typed command labels, textnavigation, and the like, wherein the interface is configured to provideinformation to the user and accept input from the user. Graphical userinterface may accept user input, wherein user input may include aninteraction with a user device. A user device may include computingdevice 104, a “smartphone,” cellular mobile phone, desktop computer,laptop, tablet computer, internet-of-things (IOT) device, wearabledevice, among other devices. User device may include any device that iscapable for locating and/or ordering alimentary elements via a datanetwork technology such as 3G, 4G/LTE, Wi-Fi (IEEE 802.11 familystandards), and the like. User device may include devices thatcommunicate using other mobile communication technologies, or anycombination thereof, for short-range wireless communication (forinstance, using Bluetooth and/or Bluetooth LE standards, AirDrop, Wi-Fi,NFC, etc.), and the like.

Continuing in reference to FIG. 1, computing device 104 is configured toreceive secondary input relating to a second user for an alimentaryelement, wherein the second user is not associated with an alimentaryelement program 108. As used in this disclosure, “secondary input,” isinput for an alimentary element by a user that has not providedbiological extraction data for the purpose of determining compatiblealimentary elements. Secondary input 116 may include input, as describedabove for primary input, that is received by computing device 104 via agraphical user interface. Secondary input 116 may include input from auser via an interaction with a user device, as described above.

Continuing in reference to FIG. 1, computing device 104 is configured togenerate an extensible alimentary element display for the second user,wherein generating the extensible alimentary element display includeslocating alimentary element originators as a function of the secondaryinput and the compatible alimentary elements for the first user as afunction of a first position associated with the first user. An“extensible alimentary element display,” as used in this disclosure, isa display using an interaction technique, user interface technique,and/or input technique functioning as a combination of hardware andsoftware elements that provides a way for at least a second user thathas not provided biological extraction data, to locate, select, and/ororder alimentary elements alongside a first user which has compatiblealimentary elements provided. An extensible alimentary element display120 is “extensible” in that the first user may not need to use such adisplay feature when inputting a request for a compatible alimentaryelement, but may require such a display feature when inputting multiplerequests, for instance for multiple users, at least one of which is notassociated with the alimentary element program 108 of the first user. An“interaction technique” starts when a user interacts with anapplication, causing an electronic device to respond, and includesdirect feedback from the device to the user.

Continuing in reference to FIG. 1, an “alimentary element originator,”as used in this disclosure, is any establishment and/or entity that mayprovide an alimentary element. An alimentary element originator mayinclude a grocer, convenience store, fast-food chain, restaurant, healthfood store, nutrition supplement store, juice bar, or the like. Analimentary element originator may be simply referred to herein as an“originator.”

Continuing in reference to FIG. 1, extensible alimentary element display120 may include interaction platforms of differing levels ofgranularity, wherein interactions may take the form of alimentaryelement requests by a plurality of users. Interaction techniques areusually characterized at various levels of granularity, or the level ofdetail, or summarization, of the units of data in the database,computing network, server, and the like. For instance, the level oftechnology, platform, and/or implementation-dependent software andhardware for implementing the interaction platform. In non-limitingillustrative examples, extensible alimentary element display 120 maydisplay to users the number and status of alimentary element originatorsaccording to their location for a single alimentary element request,representing a case of low-level granularity; however, the “hiddenlayers” of the extensible alimentary element display 120 operating onthe computing device 104 may include data corresponding to the nutritionlabels of the alimentary elements, such as caloric content, watersoluble vitamins, fat soluble vitamins, macronutrient content,micronutrient content, the identity and number of compatible alimentaryelements at each originator, among other data. In such an example, thegranularity of the “hidden layers” of the extensible alimentary elementdisplay 120—not shown to the users—may carry data that is useful tocomputing device 104 for determining which alimentary elements todisplay, which originators to locate, etc. Interaction techniques existthat are specific to various devices, such as mobile devices,touch-based displays, traditional mouse/keyboard inputs, and otherparadigms, in other words, they are dependent on a specific technologyor platform. In contrast, viewed at higher levels of granularity, theinteraction is not tied to any specific technology or platform. Theinteraction of ‘filtering’ alimentary elements, for example, can becharacterized in a way that is technology-independent, for instance,performing an action where some information is hidden and only a subsetof the original information remains or is displayed. Persons skilled inthe art, upon review of the disclosure in its entirety, will be aware ofsuch an interaction that may be implemented using any number oftechniques, and on any number of platforms and technologies.

Continuing in reference to FIG. 1, extensible alimentary element display120 may receive an interaction task, or “the unit of an entry ofinformation by the users”, such as entering a datum of text, issuing acommand, or specifying a 2D position, for instance on a map. Forinstance, and without limitation, a first user's current location, as anaddress, GPS coordinates, or the like, that may specify a position on a2D map, an indication that a specific type of meal (breakfast, lunch,dinner, etc.) is wanted, a specific price range for an alimentaryelement, or the like. A similar concept is that of domain object, whichis a datum of application data that may be manipulated by the user, oreven by the display application, such as scrolling through a queue,selecting a graphical icon, etc. Interaction techniques are the “glue”between physical I/O devices and interaction tasks, or domain objects.Different types of interaction techniques may be used to map a specificdevice to a specific domain object, for instance, in identifying auser's first position and identifying which establishments within afirst radius of that location are alimentary element originators.

Continuing in reference to FIG. 1, extensible alimentary element display120 may include any user interface (UI), graphical user interface (GUI),or interaction technique and/or method suitable for allowing user tosubmit primary input, secondary input, provide originators, alimentaryelements, and the like. Extensible alimentary element display 120 mayinclude 3D interaction techniques, different types of user interfaces,input devices, interaction designs, interactivity, informationvisualization, visual analytics, and graphical widgets (graphicalcontrol elements/controls). Persons skilled in the art, upon review ofthis disclosure in its entirety, will be aware the various methods,techniques, and technology suitable for implementing the extensiblealimentary element display 120 for receiving primary input and secondinput data and providing alimentary elements.

Continuing in reference to FIG. 1, locating alimentary elementoriginators as a function of the secondary input and the compatiblealimentary elements for the first user as a function of a first positionassociated with the first user may include using a radial searchalgorithm. A “first position,” as used in this disclosure, is a currentlocation of a user. A first position may be the location of the firstuser, which may be the same location as a second user. Alternatively oradditionally, the first user and second user may not be in the samelocation, in which case multiple locations may be used, wherein a firstuser wishes to place input for multiple alimentary elements destined fordistinct locations and to be generated by distinct originators.Computing device 104 may determine a current location of a user (firstposition) by using a mapping algorithm, application, web-based mappingtool, or the like, for instance and without limitation GOOGLE MAPS andthe Internet communicating with the GPS on a user device.

Continuing in reference to FIG. 1, computing device 104 may identifyoriginators as a function of a hierarchy of instruction, for instance byfirst identifying an originator that can provide a compatible alimentaryelement for a first user, and that has at least a second option for thesecond user. Computing device 104 may locate at least an originator thatprovides a first compatible alimentary element for the first user, and asecond, distinct compatible alimentary element that is compatible forthe first user but is intended for the second user, etc.

Continuing in reference to FIG. 1, locating alimentary elementoriginators may include using a radial search algorithm and/or anysearching algorithm for instance radial or quadrant search, “nearestneighbors” machine-learning algorithms (nearest neighbor search), amongother machine-learning algorithms, processes, methods, andnon-machine-learning algorithms. For instance, computing device 104 mayreceive a first position datum from a user, for instance using GPScapability on a user device, and a mapping application such as GOOGLEMAPS, to identify the user first position on a 2D map. Computing device104 may then search within a predefined distance, such as “walkingdistance”, an arbitrary distance value (such as 1 mile), among otherdistance parameters, and retrieve signifiers associated withoriginators, such as restaurant names, menu items, and the like.Computing device 104 may then search menus, items lists, online datarepositories, such as restaurant websites, and the like, for instanceusing a word-based query, to search for compatible terms to identify ifan originator represents a solution.

Continuing in reference to FIG. 1, computing device 104 may locatealimentary element originators using a radial search machine-learningprocess to determine a first position and search a first distancerelative to the first position for an originator. Locating an originatormay include using a radial search machine-learning process, wherein theradial search machine-learning process determines a first search radiusand searches the first radius for an alternative alimentary elementoriginator, wherein the user can order at least a compatible alimentaryelement from the originator. A radial search machine-learning processmay include machine-learning algorithms, processes, and/or models,performed by a machine-learning module, as described in further detailbelow. Radial search machine-learning process may execute a radialsearch, wherein the radial search may find approximate solutions tocombinatorial problems. Combinatorial problems involve finding agrouping, ordering, clustering, or assignment of a discrete, finite setof objects (originators) that satisfies given conditions (compatiblealimentary elements, cuisine-type, meal-type, price, etc.).

Continuing in reference to FIG. 1, a radial search machine-learningprocess may accept an input of a user geophysical location and a primaryinput and/or secondary input, and search within a first radius for anoriginator. Computing device 104 may then search the originator for analimentary element that satisfies a criterion (compatible, meal-type,cuisine type, nutrient content, etc.), generating an output thatdescribes the alimentary element, geophysical location, originatoridentity, among other data. Alternatively or additionally, a radialsearch machine-learning process may begin with a first originatorgeophysical location and menu, ingredient list, etc. as a “localsolution” and select the first originator geophysical location as thecenter for a subsequent radial search. Radial search machine-learningprocess may place the primary input and/or secondary input data on a2-Dimensional grid, for instance and without limitation, using a mappingapplication or algorithm such as a web-based navigation applicationsuch, a mobile navigation application, or the like, that may relategeophysical location in a predetermined area based on a first positionusing a computing device 104 and/or user device.

Continuing in reference to FIG. 1, radial search approach may includeusing the concept of distance rings, wherein each ring is a particulardistance about a central location, which defines the location and sizeof search areas, perhaps about a current ‘good’ solution. For instance,a first originator that provides ‘breakfast’ and at least one compatiblealimentary element may be a current ‘good’ solution, but a radial searchmay indicate a larger ring about the originator, searching further fromthat location for a second originator that provides ‘lunch’, ‘ChineseFood’, and at least a second compatible alimentary element. Radialsearch iteratively modifies the radii of these rings, and generates newcenters, to cover the search space. A concentration step corresponds tochoosing a solution (originator) as the center of a new ring. Anexpansion step corresponds to the exploration around a given center byincreasing and reducing the radius of the ring until a better solutionother than the current center is found. A “better solution” may includean originator that is nearer to a user, contains a compatible alimentaryelement, contains a specific modifier such as cuisine type, nutrientlevel, price, among other criteria. This dynamic process of centrationand expansion of the search is repeated until a stopping condition ismet. A stopping condition, for instance and without limitation, may bean originator that supplies an alimentary element a user has indicatedis suitable, or otherwise a match to a compatible alimentary element inthe alimentary element program 108 queue, and/or an alimentary elementthat is a minimal distance from user current first position.

Continuing in reference to FIG. 1, radial search may use any form ofproximity search or any algorithm used for solving an optimizationproblem of locating a point (originator) in a given set that is closestto a given point (user), provided a searching criterion (primaryinput/secondary input). Radial search algorithms, methods, andcomputational processes that radial search machine-learning process, asdescribed herein, may include exact methods of proximity searchincluding linear search and space partitioning; approximation methodssuch as Greedy search in proximity neighborhood graphs, locality sensinghashing, nearest neighbors search in spaces with small intrinsicdimension, projected radial search, vector approximation filing, andcompression/clustering based search. Alternatively or additionally,radial search machine-learning processes may include variants of radialsearch methods and algorithms such as k-nearest neighbors, approximatenearest neighbors, fixed-radius near neighbors, and all nearestneighbors.

Continuing in reference to FIG. 1, locating alimentary elementoriginators may include identifying alimentary element originators as afunction of proximity to first position, and sorting the alimentaryelement originators based on the ability to provide compatiblealimentary elements. A search criterion for locating originators mayinclude the propensity to provide compatible alimentary elements basedon a first user's biological extraction. Once an originator is locatedin this manner, computing device 104 may identify a menu, item list, orthe like, and search for alimentary elements a second user may want.Second user may submit secondary input that indicates a variety of datasuch as desired price range, cuisine type, meal type, nutrition level,allergies, food intolerances, and the like, to refine the originatorsearch. Computing device 104 may sort originators located relative tothe first position by data elements identified in the primary input,such as the presence of a particular compatible alimentary element. Oncea first solution (originator) is located, all other originators may besorted, filtered, omitted, and returned as solutions based on theability to provide at least a compatible alimentary element. Originatorswhich cannot provide a compatible alimentary element, may be queuedspecifically in response to secondary input, wherein the user has noalimentary element program 108.

Continuing in reference to FIG. 1, the extensible alimentary elementdisplay 120 for the second user may include an interactive graphicaluser interface that is configured to display alimentary elements on asecond device associated with the second user as a function of what isdisplayed on a first device associated with the first user. Extensiblealimentary element display 120 may be shown to the first user on a firstuser device. Extensible alimentary element display 120 may be initiatedon a first user device by the first user and shared with a second userso that the first user may select compatible alimentary elements from analimentary element program 108, whereas the second user may findalimentary elements either at the same originator and/or differentoriginator. Extensible alimentary element display 120 may displaycompatible alimentary elements to the first user but display differentalimentary elements to a second user, whereas the display is informedbased on location, nutrition, of submission by the first and/or seconduser, etc.

Continuing in reference to FIG. 1, computing device 104 is configured togenerate a queue of alimentary elements retrieved from the locatedalimentary element originators, wherein the queue includes alimentaryelements for the second user as a function of compatible alimentaryelements provided to the first user. A “queue of alimentary elements,”as used in this disclosure, is a collection of alimentary elements thatare maintained in a sequence and can be modified by the addition ofentities and removal of entities from the sequence via an interactiveinterface with a user. In non-limiting illustrative examples, the queuemay have an “active end” and a “reserve end,” wherein the active end isthe ‘most appropriate alimentary element’ to be displayed such as bylocation, or some other discriminating criteria that has been determinedby computing device 104 in a nearby originator; additionally, there maybe related alimentary elements that are in the queue “behind” the firstactive end alimentary element and alternatives nearer the reserve end.In further non-limiting illustrative examples, a user may indicate viathe graphical user interface that they do not want an alimentaryelement, whereby computing device 104 may remove it from the active endand push up by one place the next alimentary elements in the queue. Insuch an example, computing device 104 may add a newly generatedalimentary element to the reserve end to maintain a list that a user mayview, scroll through, or the like. Computing device 104 may locate anoriginator for each alimentary element in the queue; alternatively oradditionally, computing device 104 may restrict searches to the most‘active end’ entity in the queue or to an alimentary element that a useras selected.

Continuing in reference to FIG. 1, generating the queue of alimentaryelements may include selecting the alimentary element originator andretrieving a plurality of alimentary elements from at least a locatedalimentary element originator. Locating the originator may includeselection based on location, such as proximity to a first position.Locating the originator may include identifying compatible alimentaryelements in the originators inventory, menu, item lists, etc., andselecting the originator as a function of retrieving a particular numberof compatible options. Computing device 104 may iteratively locate andretrieve alimentary elements from originators using a variety ofcriteria, as described above, wherein retrieval includes locatingparticular data (alimentary element identity, prices, ingredients list,nutrition facts, Cuisine-type, online reviews, etc.) wherein the data isstored in non-transitory memory on the computing device 104 that may beselected to be displayed and arranged on the extensible alimentaryelement display 120. In non-limiting illustrative examples, computingdevice 104 may retrieve alimentary elements using a word, or term-basedquery, wherein the user has selected an alimentary element such as“salad”, and computing device 104 locates originators based on acombination of user location and items listed as “salad”. In such anexample, all alimentary elements with “salad” in the name may beretrieved, along with accompanying data; alternatively or additionally,alimentary elements with “salad” may be further filtered or sorted priorto being retrieved, for instance based on nutrition content.

Continuing in reference to FIG. 1, generating the queue of alimentaryelements may include calculating, using a machine-learning process 124,a plurality of nutrition metrics for the plurality of alimentaryelements as a function of the nutrition content. A “nutrition metric,”as used in this disclosure, is any qualitative and/or quantitativemetric that describes nutritional value of an alimentary element for anindividual. Nutrition metric 128 may include a qualitative metric, orsignifier, such as “healthy”, “not healthy,” “choose”, “avoid”, and thelike. Nutrition metric 128 may include a quantitative metric, such as anumerical value that signals the caloric content, macronutrient contentsuch as effect on increasing blood sugar level, micronutrient effect ondisease content such as iron content for addressing anemia, among othercategories. Nutrition metric 128 may include data relating to thenutrition content as calculated from the ‘nutrition facts label’ of analimentary element and a nutritional standard such as the ‘recommendeddaily allowance based on 2,000 calories. Nutrition metric 128 mayinclude a percentile that ranks alimentary elements relative to otheravailable options, for instance without limitation, where a‘chimichanga’ may be a 60^(th) percentile option for healthy Mexicancuisine but ‘tampiqueño’ is 85^(th) percentile. As used in thisdisclosure, “nutrition content,” is any qualitative and/or quantitativevalue or descriptor that relates to the nutrition content of analimentary element. Nutrition content may include qualitativedescriptors such as “no appreciable amount”. Nutrition content mayinclude numerical values such the mass in grams ofmacronutrients/micronutrients per serving size, percent of a recommendeddaily allowance, etc.

Continuing in reference to FIG. 1, machine-learning process 124 mayinclude any machine-learning process, model, and/or algorithm performedby a machine-learning module, as described in further detail below. A“machine learning process,” as used in this disclosure, is a processthat automatedly uses a body of data known as “training data” and/or a“training set” to generate an algorithm that will be performed by acomputing device/module to produce outputs given data provided asinputs; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language. Training data for amachine-learning process 124 may be used for generating amachine-learning model using the training data. Training data mayinclude recommended daily allowances of macronutrients (carbohydrates,fats, and protein), micronutrients (water-soluble vitamins, fat-solublevitamins, trace metals, co-factors, etc.), and the like, that isrecommended of a particular user, such as based on a standard 2,000calorie diet, ketogenic diet, vegan diet, Atkins diet, etc. Trainingdata may include data retrieved from originators, including nutritionfacts, where an entire menu of alimentary elements and the recommendeddaily allowance may be used to generate nutrition metrics 128 that wouldplace each alimentary element on a numerical value scale, such as astandard percentile (0-100) scale for direct comparison betweenalimentary elements.

Continuing in reference to FIG. 1, a machine-learning process 124 maygenerate a plurality of nutrition metrics 128 by using training data togenerate a machine-learning model, wherein the machine-learning modelcontains correlations, heuristics, and/or any mathematical relationshipsthat may be determined from the training data. Machine-learningalgorithm may include a supervised machine-learning algorithms, such aslinear regression, k-nearest neighbors, naïve Bayes, neural networks,among other suitable supervised learning algorithms. Machine-learningalgorithm may include unsupervised machine-learning algorithms, such asdimensionality reduction, clustering algorithms, among other suitableunsupervised learning algorithms. Calculating nutrition metric 128 usinga machine-learning algorithm may include generating a graphical analysisdescribing, for instance and without limitation, the average caloriccontent per menu item, average micronutrient deficiency per cuisinetype, among other relationships. In such an example, alimentary elementsmay be clustered into distinct queues based on these trends (forinstance, diabetes-compatible cuisine types, anemia-addressing cuisinetypes, high-protein cuisine types) for displaying to the users.Alimentary elements may be placed into a queue as a function of anutrition metric 128, such as an “edible score,” which reflects thenutritional impact of an alimentary element and potential effect on auser's health, such as is determined and described in U.S.Nonprovisional application Ser. No. 16/983,034 filed on Aug. 3, 2020,and entitled “METHODS AND SYSTEMS FOR CALCULATING AN EDIBLE SCORE IN ADISPLAY INTERFACE,” the entirety of which is incorporated herein byreference. An edible score may be a numerical value that described thenutrition content of an alimentary element and may be used for ranking,weighting, or otherwise filtering alimentary elements, for instanceaccording to a threshold value, for building a queue. Such a queue maydirect the extensible alimentary element display 120 as to whichalimentary elements to display to second user and in what order.

Continuing in reference to FIG. 1, the plurality of alimentary elementsmay be sorted according to nutrition content. Alimentary elements may bedisplayed to the second user as a function of nutrition content, whereinalimentary elements may be sorted, ranked, and/or weighted based onnutrition metric 128. Alimentary elements may be sorted using a rankingfunction. Ranking function may include using computing device 104 toarrange alimentary elements in a particular ordering, or rank, based onthe nutrition metrics 128, for instance from greatest numerical value toleast. A ranking function may include machine-learning algorithms,processes, and/or models where alimentary elements with correspondingnutrition metrics 128 are used as inputs and an output of an ordered, orranked, set of alimentary elements is generated based on relationshipscaptured in a machine-learning model from training data. In such a case,training data may include a plurality of past selected alimentaryelements by “guest users” (multiple users that are not the first user),wherein a plurality of alimentary elements that are commonly selectedalongside compatible alimentary elements may receive a higher ranking(weighted) than nutrition metric 128 would imply. Ranking criterion usedby machine-learning process may include additional data alongsidenutrition content such as meal-type, Cuisine-type, price, or the like. Amachine-learning process used for accepting inputs and generating aranked output as a function of some criteria may include any algorithmdescribed herein, as described in further detail below. Rankingalimentary elements may assist extensible alimentary element display 120in ‘knowing’ which alimentary elements to place into a queue, in whichorder to place into the queue, and which to display to the first userand/or second user.

Continuing in reference to FIG. 1, generating the queue of alimentaryelements may include filtering the plurality of alimentary elements as afunction of the plurality of nutrition metrics 128 and a thresholdvalue. A “threshold value,” as used in this disclosure, is a qualitativeand/or quantitative value, or criterion, used for determining whichalimentary elements to place into a queue. In non-limiting illustrativeexamples, threshold value 132 may include a qualitative criterion suchas “breakfast type elements,” wherein only alimentary elements that areinclude identification data signifying “breakfast” are placed into thequeue, whereas other alimentary elements that would otherwise bedisplayed are stored in a database 136. In such a case, alimentaryelements (ranked or not) may be filtered for the extensible alimentaryelement display 120 using a defining term. In further non-limitingillustrative examples, threshold value 132 may include a quantitativevalue such as “only alimentary elements above a nutrition metric 128 of‘70’,” wherein only alimentary elements with nutrition metrics 128 abovea certain numerical value are included into the queue. In such a case,alimentary elements (ranked or not) may be filtered for the extensiblealimentary element display 120 using nutrition content as signified by anutrition metric 128. Computing device 104 may set a threshold value132, for instance and without limitation, as a function of userinteraction with a user device and build an alimentary element queue 140as a function of the threshold value 132.

Referring now to FIG. 2, an exemplary embodiment 200 of a compatiblealimentary element database 204 is illustrated. Alimentary elements forthe second user may include being stored in compatible alimentaryelement database 204. Compatible alimentary elements from an alimentaryelement program 108 for a first user may also be stored and/or retrievedfrom a compatible alimentary element database 204. Computing device 104may store and/or retrieve primary input 112, secondary input 116,alimentary element data, first position data, compatible alimentaryelements, nutrition metrics 128, among other determinations, I/O data,and the like, in a compatible alimentary element database 204.Compatible alimentary element database 204 may be implemented, withoutlimitation, as a relational database, a key-value retrieval databasesuch as a NOSQL database, or any other format or structure for use as adatabase that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Compatible alimentaryelement database 204 may alternatively or additionally be implementedusing a distributed data storage protocol and/or data structure, such asa distributed hash table and the like. Compatible alimentary elementdatabase 204 may include a plurality of data entries and/or records, asdescribed above. Data entries in a compatible alimentary elementdatabase 204 may be flagged with or linked to one or more additionalelements of information, which may be reflected in data entry cellsand/or in linked tables such as tables related by one or more indices ina relational database. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which dataentries in a database may store, retrieve, organize, and/or reflect dataand/or records as used herein, as well as categories and/or populationsof data consistent with this disclosure. Computing device 104 mayretrieve any determinations, as described herein, from the compatiblealimentary element database 204, such as ranked alimentary elements,threshold values 128, nutrition metrics 128, located originators, andthe like.

Further referring to FIG. 2, compatible alimentary element database 204may include, without limitation, alimentary element program table 208,alimentary element originator table 212, nutrition metric table 216,threshold value table 220, alimentary element queue table 224, and/orheuristic table 228. Determinations by a machine-learning process,machine-learning model, ranking function, and/or mapping application,may also be stored and/or retrieved from the compatible alimentaryelement database 204, for instance in non-limiting examples, alimentaryelement originators located as a function of a first position, the firstposition, and originators that a user preferably frequents. As anon-limiting example, compatible alimentary element database 204 mayorganize data according to one or more instruction tables. One or morecompatible alimentary element database 204 tables may be linked to oneanother by, for instance in a non-limiting example, common columnvalues. For instance, a common column between two tables of compatiblealimentary element database 204 may include an identifier of asubmission, such as a form entry, textual submission, accessory devicetokens, local access addresses, metrics, and the like, for instance asdefined herein; as a result, a search by a computing device 104 may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of data, including types of data,names and/or identifiers of individuals submitting the data, times ofsubmission, and the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which datafrom one or more tables may be linked and/or related to data in one ormore other tables.

Continuing in reference to FIG. 2, in a non-limiting embodiment, one ormore tables of a compatible alimentary element database 204 may include,as a non-limiting example, an alimentary element program table 208,which may include categorized identifying data, as described above,including compatible alimentary element identities, nutrition content,originator identities, prices, Cuisine type, meal type, and the like.One or more tables may include alimentary element originator table 212,which may include data regarding originators used in the past,originators that offer compatible alimentary elements and those that donot, originators organized by Cuisine type, meal type, price, etc., thatsystem 100 may use to retrieve and/or store alimentary elementoriginator locations, menus, identities, and the like. One or moretables may include nutrition metric table 216, which may includenutrition metrics 128, nutrition content of alimentary elements, theidentities of the alimentary elements, and the like, that system 100 mayuse for determining nutrition metrics 128. One or more tables mayinclude threshold value table 220, which may include files of thresholdvalues 128, past threshold values 128, and the like, as described abovefor instance and without limitation, that system 100 may use toretrieve, sort, and/or store, for filtering alimentary elements. One ofmore tables may include an alimentary element queue table 224, which mayinclude instructions, numerical values, and/or outputs, determinations,variables, and the like, organized into subsets of data for generatinginstructions for how to build alimentary element queue 140 forextensible alimentary element display 120. One or more tables mayinclude, without limitation, a heuristic table 228, which may organizerankings, scores, models, outcomes, functions, numerical values, arrays,matrices, and the like, that represent determinations, probabilities,metrics, parameters, and the like, include one or more inputs describingpotential mathematical relationships, as described herein.

Referring now to FIG. 3, a non-limiting exemplary embodiment 300 ofgenerating a file share label for transmitting an alimentary elementfile between devices is illustrated. Providing a representation of atleast a compatible alimentary element for the first user and at least analimentary element for the second user may include generating a fileshare label. A “file share label,” as used in this disclosure, is anaccess token containing security credentials computing devices and/oruser devices may use to identify one another and communicate. Computingdevice 104 may establish communication with at least a user device (suchas a first device 304 and a second device 308) by generating a fileshare label 312. Computing device 104 may generate a unique file sharelabel 312 for each device and/or may establish a file share label 312for all user devices communicating with computing device 104. Computingdevice 104 may generate and transmit a file share label 312 toauthenticate with a plurality of user devices and/or for the pluralityof user devices to communicate with a compatible alimentary elementdatabase 204. File share label 312 may include an identifier associatedwith a logon session, wherein the identifier contains credentials toinitiate communication between a first device 304 and a second device308. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in access tokens may begenerated and shared among devices.

Still referring to FIG. 3, a file share label 312 may includecommunication exchange such as a ‘telecommunication handshake’ thatincludes an automated process of communications between two or moredevices, such as a first device 304 and a second device 308. Atelecommunication handshake includes the exchange of informationestablishing protocols of communication at the start of communicationbefore full communication commences. A telecommunication handshake mayinclude exchanging signals to establish a communication link as well asto agree as to which protocols to implement. A telecommunicationhandshake may include negotiating parameters to be utilized betweensubject user device and computing device 104, including informationtransfer rate, coding alphabet, parity, interrupt procedure, and/or anyother protocol or hardware features. A telecommunication handshake mayinclude but is not limited to a transmission control protocol (TCP),simple mail transfer protocol (SMTP), transport layer security (TLS),Wi-Fi protected access (WPA), and the like.

Continuing in reference to FIG. 3, providing a representation of atleast a compatible alimentary element for the first user and at least analimentary element for the second user may include transmitting analimentary element file from a first device 304 associated with thefirst user to a second device 308 associated with the second user,wherein the alimentary element file contains instructions forrepresenting, via a graphical user interface, the alimentary element onthe second device. An “alimentary element file,” as used in thisdisclosure, is a data file containing instruction for representingalimentary elements on the second device 308 as a function of what isdisplayed on a first device 304. An alimentary element file 312 mayinclude data for guiding what to display from a first user to aplurality of “guest” users. In this way, data describing what isdisplayed to a second user is sent to the second user device. Analimentary element file 312 may include originators displayed as afunction of a first position associated with at least a first user,second user, and/or plurality of users, as described above. An alimentelement file 312 may include nutrition metrics 128 associated with thedisplayed alimentary elements organized into a queue. An alimentaryelement file 312 may be transmitted using any radio frequencies andelectromagnetic frequencies between approximately 20 kHz andapproximately 300 GHz intended for communication between electronicdevices, for instance as commonly used between network interfaces andlocal wireless communication. In exemplary embodiments, file share label312 and/or alimentary element file 312 may include radio frequency (RF)transceiver components for accessing wireless voice and/or data networks(for instance, using cellular telephone technology, data networktechnology such as 3G, 4G/LTE, Wi-Fi (IEEE 802.11 family standards), orother mobile communication technologies, or any combination thereof),components for short-range wireless communication (for instance, usingAirdrop, Bluetooth and/or Bluetooth LE standards, NFC, etc.), and/orother components. Network interface may provide wired networkconnectivity (such as Ethernet) in addition to and/or instead of awireless interface. Network interface may be implemented using acombination of hardware (for instance, driver circuits, antennas,modulators/demodulators, encoders/decoders, and other analog and/ordigital signal processing circuits) and software components. Networkinterface may support multiple communication channels concurrently,using the same transport or different transports, as necessary.

Continuing in reference to FIG. 3, computing device 104 is configured toprovide a representation via the extensible alimentary element display120 of at least a compatible alimentary element for the first user andat least an alimentary element for the second user as a function of thequeue of alimentary elements. Alimentary element file 312 may include atleast a compatible alimentary element, for instance as retrieved from analimentary element program 108 intended for first user. Alimentaryelement file 312 may include at least an alimentary element for seconduser. Alimentary element file 312 may include an alimentary elementqueue 140 for directing extensible alimentary element display 120 to‘know’ which alimentary elements to display, and in which order.Alimentary element file 312 may include data for the representation viathe extensible alimentary element display 120, including order time,nutrition metric 128, nutrition facts, biological extraction (for afirst user), and the like. A representation via the extensiblealimentary element display 120 may be generated using any user interfaceand/or graphical user interface, as described above, including graphics,textual output, and the like.

Referring now to FIG. 4, a non-limiting exemplary embodiment 400 of anindication of the alimentary element by a respective interaction by thefirst user with the first device is signaled to the second device via anaudiovisual notification is illustrated. An “audiovisual notification,”as used in this disclosure, is a piece of information that alerts a userto an alimentary element. An audiovisual notification 404 may be atextual alert, a graphic, a vibration alert, a sound, or any otheraudiovisual notification, haptic feedback from a user device, orcombination thereof, that computing device 104 may provide a user.Audiovisual notification 404 may include addressing the first user toselect an alimentary element by a first device 304, for instance fromthe plurality of compatible alimentary elements from an alimentaryelement program 108. Audiovisual notification 404 may include addressingthe second user to select an alimentary element by a second device 308,for instance from the plurality of alimentary elements in a queue in theextensible alimentary element display 120. Audiovisual notification 404may include prompting the user to provide input, such as a cuisine type(Korean food), meal type (dinner), diet type (Paleo diet), price range(<$30 per entrée), and the like. Audiovisual notification 404 mayinclude alerting a user to a potential allergen (shellfish, tree nuts,etc.), food intolerance (lactose, gluten, etc.), or other alimentaryelement consideration. Audiovisual notification 404 may includenutrition metric 128 information. Audiovisual notification 404 mayinclude a time period for which a plurality of users may submitalimentary elements as “guests” to a first device 304 that initiatedordering.

Referring now to FIG. 5, an exemplary embodiment 500 of locatingalimentary element originators is illustrated. Radial search may be usedfor locating alimentary element originators as a function of the firstposition of a first user, second user, and/or plurality of users.Computing device 104 may use a radial search machine-learning process,as described herein, to locate alimentary element originators within afirst radius. As depicted in FIG. 2A, radial search may select a firstsearch radius to search based on a user first position (black-shadedcircle), wherein a first circle (dashed line) of area about the user issearched for a suitable originator. In the event that a suitableoriginator is not located, the radius may widen to larger radiiconcentric rings (larger dashed-line rings). Alternatively oradditionally, as depicted in FIG. 2B, a first radius may be searchedabout a first local solution that is a first alimentary elementoriginator (black-shaded circle) until a better solution is located(grey-shaded circle), and in the even the alimentary originator is notsuitable, or the user indicates a different alimentary element, or thesolution is otherwise not optimal, a second radius may be searched,which may locate additional originators (white circle). Each additionalsearch radii may be larger or smaller than a previous search radius butmay include a different search center. Originator locations may befiltered as a function of both compatible alimentary elements and atleast a second alimentary element suitable for a second user.

Referring now to FIG. 6, an exemplary embodiment of a machine-learningmodule 600 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 604 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 608 given data provided as inputs 612;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 6, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 604 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 604 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 604 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 604 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 604 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 604 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data604 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 6,training data 604 may include one or more elements that are notcategorized; that is, training data 604 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 604 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 604 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailherein. Training data 604 used by machine-learning module 600 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 6, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailherein; such models may include without limitation a training dataclassifier 616. Training data classifier 616 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedherein, such as a mathematical model, neural net, or program generatedby a machine learning algorithm known as a “classification algorithm,”as described in further detail herein, that sorts inputs into categoriesor bins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 600 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 604. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 616 may classify elements of training data to elementsthat characterizes a sub-population, such as a subset of alimentaryelements as a function of nutrition metrics 128 or a subset ofpreferable originator locations and/or other analyzed items and/orphenomena for which a subset of training data may be selected.

Still referring to FIG. 6, machine-learning module 600 may be configuredto perform a lazy-learning process 620 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofpredictions may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 604. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 604elements, such as ranking alimentary elements and building a queue as afunction of some ranking association between elements (nutrition metric128). Lazy learning may implement any suitable lazy learning algorithm,including without limitation a K-nearest neighbors algorithm, a lazynaïve Bayes algorithm, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail herein.

Alternatively or additionally, and with continued reference to FIG. 6,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 624. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 624 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 624 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 604set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. A machine-learning model may be used as a rankingfunction, as described above, to improve building an alimentary elementqueue 140 by “learning” which alimentary elements should be ranked aboveothers based on, for instance, nutrition content, user preferences,locations, and the like.

Still referring to FIG. 6, machine-learning algorithms may include atleast a supervised machine-learning process 628. At least a supervisedmachine-learning process 628, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude a plurality of alimentary elements and nutrition metrics 128 asdescribed above as inputs, a queue of alimentary elements as outputs,and a ranking function representing a desired form of relationship to bedetected between inputs and outputs; ranking function may, for instance,seek to maximize the probability that a given input and/or combinationof elements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Ranking function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 604. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 628 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 6, machine learning processes may include atleast an unsupervised machine-learning processes 632. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 6, machine-learning module 600 may be designedand configured to create a machine-learning model 624 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g., a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g., a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 6, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors' algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 6, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training data 604 set are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data 604.

Referring now to FIG. 7, an exemplary embodiment of a method 700 forproviding alimentary elements is illustrated. At step 705, computingdevice 104 is configured for receiving primary input 112 relating to afirst user for a compatible alimentary element, wherein compatiblealimentary elements are based on biological extraction received from thefirst user; this may be implemented, without limitation, as describedabove in reference to FIGS. 1-6.

Continuing in reference to FIG. 7, at step 710, computing device 104 isconfigured for receiving secondary input 116 relating to a second userfor an alimentary element, wherein the second user is not associatedwith an alimentary element program 108; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-6.

Still referring to FIG. 7, at step 715, computing device 104 isconfigured for generating an extensible alimentary element display 120for a second user, wherein generating the extensible alimentary elementdisplay 120 includes locating alimentary element originators as afunction of a secondary input and the compatible alimentary elements forthe first user as a function of a first position associated with thefirst user, and generating a queue of alimentary elements retrieved fromthe located alimentary element originators, wherein the queue includesalimentary elements for the second user as a function of the secondaryinput and the compatible alimentary elements provided to the first user.Locating alimentary element originators may include identifying thealimentary element originators as a function of proximity to the firstposition and sorting the alimentary element originators based on theability to provide compatible alimentary elements. The extensiblealimentary element display 120 for the second user may include aninteractive graphical user interface that is configured to displayalimentary elements on a second device associated with the second useras a function of what is displayed on a first device associated with thefirst user. Generating the queue of alimentary elements may includeselecting the alimentary element originator and retrieving a pluralityof alimentary elements from at least a located alimentary elementoriginator. Generating the queue of alimentary elements may includecalculating, using a machine-learning process, a plurality of nutritionmetrics 128 for the plurality of alimentary elements as a function ofthe nutrition content. The plurality of alimentary elements may besorted according to nutrition content. Generating the queue ofalimentary elements may include filtering the plurality of alimentaryelements as a function of the plurality of nutrition metrics 128 and athreshold value 132. The alimentary elements for the second user may bestored in compatible alimentary element database 204; this may beimplemented, without limitation, as described above in reference toFIGS. 1-6.

Continuing in reference to FIG. 7, at step 720, computing device 104 isconfigured for providing a representation, via a graphical userinterface, of at least a compatible alimentary element for the firstuser and at least an alimentary element for the second user. Providing arepresentation of at least a compatible alimentary element for the firstuser and at least an alimentary element for the second user may includegenerating a file share label 312 and transmitting an alimentary elementfile 316 from a first device 304 associated with the first user to asecond device 308 associated with the second user, wherein thealimentary element file 316 contains instructions for representing, viaa graphical user interface, the alimentary element on the second device308. An indication of the alimentary element by a respective interactionby the first user with the first device 304 may be signaled to thesecond device 308 via an audiovisual notification 404; this may beimplemented, without limitation, as described above in reference toFIGS. 1-6.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC)

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for providing alimentary elements, thesystem comprising: a computing device, wherein the computing device isconfigured to: receive a primary input relating to a first user for acompatible alimentary element, wherein the compatible alimentary elementis selected as a function of a request received from the first user;receive a secondary input relating to a second user for a guidance-freealimentary element; and generate an extensible alimentary elementdisplay, wherein generating the extensible alimentary element displayfurther comprises: locating at least an alimentary element originator asa function of the secondary input and the compatible alimentary element;and displaying a queue of alimentary elements from the at least analimentary element originator, wherein displaying the queue furthercomprises: displaying a first alimentary element for the first userrelating to the request; and displaying a second alimentary element forthe second user relating to the first alimentary element.
 2. The systemof claim 1, wherein the request identifies a meal.
 3. The system ofclaim 1, wherein the request is generated as a function of aquestionnaire.
 4. The system of claim 1, wherein the request isgenerated as a function of a delivery time.
 5. The system of claim 1wherein receiving the primary input further comprises: retrieving anutrition metric from a previously consumed meal; and generating therequest as a function of the nutrition metric.
 6. The system of claim 5,wherein generating the request further comprises generating a machinelearning process, wherein the machine learning process utilizes thenutrition metric as an input and outputs the request.
 7. The system ofclaim 1, wherein displaying the queue of alimentary elements furthercomprises: assigning the first alimentary element to a category; andselecting the second alimentary element as a function of the category.8. The system of claim 1, wherein displaying the second alimentaryelement further comprises: receiving from the second user a lifestyleconsideration; classifying the lifestyle consideration to an eatingstyle as a function of a machine learning process; and displaying asecond alimentary element related to the eating style.
 9. The system ofclaim 1, wherein displaying the first alimentary element for the firstuser further comprises displaying a plurality of alimentary elementswherein each of the plurality of alimentary elements is ranked anddisplayed as a function of a nutrition metric.
 10. The system of claim1, wherein displaying the second alimentary element for the second userfurther comprises displaying a plurality of alimentary elements whereineach of the plurality of alimentary elements is ranked and displayed asa function of the request received from the first user.
 11. A method ofproviding alimentary elements, the method comprising: receiving by acomputing device, a primary input relating to a first user for acompatible alimentary element, wherein the compatible alimentary elementis selected as a function of a request received from the first user;receiving by the computing device, a secondary input relating to asecond user for a guidance-free alimentary element; and generating bythe computing device, an extensible alimentary element display, whereingenerating the extensible alimentary element display further comprises:locating at least an alimentary element originator as a function of thesecondary input and the compatible alimentary element; and displaying aqueue of alimentary elements from the at least an alimentary elementoriginator, wherein displaying the queue further comprises: displaying afirst alimentary element for the first user relating to the request; anddisplaying a second alimentary element for the second user relating tothe first alimentary element.
 12. The method of claim 11, wherein therequest identifies a meal.
 13. The method of claim 11, wherein therequest is generated as a function of a questionnaire.
 14. The method ofclaim 11, wherein the request is generated as a function of a deliverytime.
 15. The method of claim 11 wherein receiving the primary inputfurther comprises: retrieving a nutrition metric from a previouslyconsumed meal; and generating the request as a function of the nutritionmetric.
 16. The method of claim 15, wherein generating the requestfurther comprises generating a machine learning process, wherein themachine learning process utilizes the nutrition metric as an input andoutputs the request.
 17. The method of claim 11, wherein displaying thequeue of alimentary elements further comprises: assigning the firstalimentary element to a category; and selecting the second alimentaryelement as a function of the category.
 18. The method of claim 11,wherein displaying the second alimentary element further comprises:receiving from the second user a lifestyle consideration; classifyingthe lifestyle consideration to an eating style as a function of amachine learning process; and displaying a second alimentary elementrelated to the eating style.
 19. The method of claim 11, whereindisplaying the first alimentary element for the first user furthercomprises displaying a plurality of alimentary elements wherein each ofthe plurality of alimentary elements is ranked and displayed as afunction of a nutrition metric.
 20. The method of claim 11, whereindisplaying the second alimentary element for the second user furthercomprises displaying a plurality of alimentary elements wherein each ofthe plurality of alimentary elements is ranked and displayed as afunction of the request received from the first user.